COASTCOLOUR. CoastColour UCM1 * ESRIN *
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1 CoastColour UCM1 * ESRIN *
2 In situ data 02/
3
4 Algorithm REQUIREMENTS These parameters are needed for basic water algorithms (for training of neural networks) The adaptation of the NN algorithm to local conditions can be performed on different levels. The most comprehensive one is the full bio-optical model, which is used for the simulation of the training data set. If the full suite is not available, any of the above listed variables is helpful including information about the range of IOPs and / or concentrations. 16/ UCM1 - ESRIN 4
5 Quality assurance Data processing: building up templates Verifying protocols Applying basic statistics for each parameter, average, max and minimum. Filter, sort ascending, etc Visual inspection: checking for internal consistence between each database Checking for consistence between different databases Comparison with published literature CoastColour UCM1 * ESRIN *
6 Data sets were introduced into common templates CoastColour UCM1 * ESRIN *
7 Frequent problems/doubts/hesitations Lack of information or contradictory information regarding sampling time Different format of coordinates. Or lack of coordinates. Errors in dates? Various parameters registered on 1 day, and then 1 parameter in a different day Difficult to decide if it is an error or not Doubts to be clarified during the meeting CoastColour UCM1 * ESRIN *
8 Checking for internal consistence, through correlation plots Spotting outliers Site 1 Diffuse attenuation coefficient and Chla 12,00 10,00 Even without correlation we can spot outliers PM S 8,00 6,00 4,00 2,00 y = 125,23x + 0,7287 R² = 0,0676 CoastColour UCM1 * ESRIN * ,00 0,000 0,005 0,010 0,015 adet665 8
9 Internal consistency temporal trends CoastColour UCM1 * ESRIN *
10 IOPs Site 1 Baltic 0,5 0,45 0,4 0,35 0,3 0,25 0, ,15 0,1 0,05 0 ap411 ap443 ap489 ap510 ap560 ap619 ap665 ap683 ap705 ap aph ad ag Particle absorption ap Phytoplankton absorption aph Non pigmented particle ad CoastColour UCM1 * ESRIN * Dissolved Material ag 10
11 Visual inspection /QC Phytoplankton absorption coefficient CoastColour UCM1 * ESRIN *
12 Visual inspection: Reflectance spectra were normalized in relation to 560nm Low Chla High Chla Site 11 CoastColour UCM1 * ESRIN *
13 Rfl Reflectance dt data Extreme value in 708!? Chla > 100 mg m -3 CoastColour UCM1 * ESRIN *
14 Quality control: Reflectance data Werdell & Bailey 2005 CoastColour UCM1 * ESRIN *
15 Band rations Rrs 443/ Rrs 510 versus Rrs 490/Rrs560 Rrs_RR_ Chla < 1 mg m-3 Rrs490/Rrs Rrs (510) Rrs (443) / Rrs443/Rrs Rrs (490) / Rrs (560) Chla 1-10 mg m-3 Chla mg m-3, Rrs (44 43) / Rrs (510) ) / Rrs (510) Rrs (443) Rrs (490) / Rrs (560) Rrs (490) / Rrs (560) 15
16 Exponential slope of CDOM absorption spectrum Site 1 Slope Babin et al, 2003 CoastColour UCM1 * ESRIN *
17 Further statistics central tendency: average, trimmed average, median, min, max, quartiles, 10% and 90%ile; dispersion: standard deviation, range, interquartile range, outliers, variance, coefficient of variation, real average range (95%confidence); * probability distributions * Time Analysis CoastColour UCM1 * ESRIN *
18 Dataset End-user Chla product Site A Site B Internal validation Validation February Test products delivered to endusers April Feedback from by Endusers (Validation made by those who want) June Level 2 products will be provided to end-users CoastColour UCM1 * ESRIN *
19 See you in Lisbon, UCM October 2011 Thank you CoastColour UCM1 * ESRIN *
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